一种基于图注意力机制的气温预测模型

 积极应对气候变化是可持续发展的目标之一。针对气温准确预测任务，提出了一种基于图注意力机制的气温预测模型。该模型在气温站点组成的拓扑结构上使用了注意力机制，选择性地聚合周围区域的气温特征，再使用神经网络拟合复杂的气温变化规律，得到预测结果。实验使用了2000-2010年京津冀地区的气温数据，经大量实验验证，在极少依赖历史气温数据的情况下，模型能够得到更准确的预测值。模型能够为气候预测和气候灾害预防提供决策支持。

Atemperature prediction model based on graph attention mechanism

The active response to the climate change is one of the goals of sustainable development. This paper presents a temperature prediction model based on the graph attention mechanism. The attention mechanism on the topology of the temperature sites is used to selectively aggregate the temperature feature of the surrounding area. Then the neural network is used to fit the complex temperature change pattern and forecast the future temperatures. In the experiments, the temperature data of Beijing-Tianjin-Hebei region from 2000 to 2010 are used. A large number of experiments show that with this method more accurate predictions can be made with a small amount of historical temperature data. The model can provide the decision support for the climate prediction and the climate disaster prevention, with an important scientific and practical significance.